OpenRaiser/Intern-Atlas huggingface dataset:
Plug and Play Bench: Simplifying Big Data Benchmarking Using Containers
Venue: arXiv cs.DC 2017
Year: 2017

The recent boom of big data, coupled with the challenges of its processing and storage gave rise to the development of distributed data processing and storage paradigms like MapReduce, Spark, and NoSQL databases. With the advent of cloud computing, processing and storing such massive datasets on clusters of machines is now feasible with ease. However, there are limited tools and approaches, which users can rely on to gauge and comprehend the performance of their big data applications deployed locally on clusters, or in the cloud. Researchers have started exploring this area by providing benchmarking suites suitable for big data applications. However, many of these tools are fragmented, complex to deploy and manage, and do not provide transparency with respect to the monetary cost of benchmarking an application. In this paper, we present Plug And Play Bench, an infrastructure aware abstraction built to integrate and simplify the deployment of big data benchmarking tools on clusters of machines. PAPB automates the tedious process of installing, configuring and executing common big data benchmark workloads by containerising the tools and settings based on the underlying cluster deployment framework. Our proof of concept implementation utilises HiBench as the benchmark suite, HDP as the cluster deployment framework and Azure as the cloud platform. The paper further illustrates the inclusion of cost metrics based on the underlying Microsoft Azure cloud platform.
Cost-Effective Active Learning for Melanoma Segmentation
Venue: arXiv cs.CV 2017
Year: 2017

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance. The source code of this project is available at https://marc-gorriz.github.io/CEAL-Medical-Image-Segmentation/ .
Optimal Gossip Algorithms for Exact and Approximate Quantile Computations
Venue: arXiv cs.DS 2017
Year: 2017

This paper gives drastically faster gossip algorithms to compute exact and approximate quantiles. Gossip algorithms, which allow each node to contact a uniformly random other node in each round, have been intensely studied and been adopted in many applications due to their fast convergence and their robustness to failures. Kempe et al. [FOCS'03] gave gossip algorithms to compute important aggregate statistics if every node is given a value. In particular, they gave a beautiful $O(\log n + \log \frac{1}ε)$ round algorithm to $ε$-approximate the sum of all values and an $O(\log^2 n)$ round algorithm to compute the exact $ϕ$-quantile, i.e., the the $\lceil ϕn \rceil$ smallest value. We give an quadratically faster and in fact optimal gossip algorithm for the exact $ϕ$-quantile problem which runs in $O(\log n)$ rounds. We furthermore show that one can achieve an exponential speedup if one allows for an $ε$-approximation. We give an $O(\log \log n + \log \frac{1}ε)$ round gossip algorithm which computes a value of rank between $ϕn$ and $(ϕ+ε)n$ at every node.% for any $0 \leq ϕ\leq 1$ and $0 < ε< 1$. Our algorithms are extremely simple and very robust - they can be operated with the same running times even if every transmission fails with a, potentially different, constant probability. We also give a matching $Ω(\log \log n + \log \frac{1}ε)$ lower bound which shows that our algorithm is optimal for all values of $ε$.
Privacy Risks from Public Data Sources
Venue: arXiv cs.CY 2017
Year: 2017

In the fight against tax evaders and other cheats, governments seek to gather more information about their citizens. In this paper we claim that this increased transparency, combined with ineptitude, or corruption, can lead to widespread violations of privacy, ultimately harming law-abiding individuals while helping those engaged in criminal activities such as stalking, identity theft and so on. In this paper we survey a number of data sources administrated by the Greek state, offered as web services, to investigate whether they can lead to leakage of sensitive information. Our study shows that we were able to download significant portions of the data stored in some of these data sources (scraping). Moreover, for those data sources that were not amenable to scraping we looked at ways of extracting information for specific individuals that we had identified by looking at other data sources. The vulnerabilities we have discovered enable the collection of personal data and, thus, open the way for a variety of impersonation attacks, identity theft, confidence trickster attacks and so on. We believe that the lack of a big picture which was caused by the piecemeal development of these data sources hides the true extent of the threat. Hence, by looking at all these data sources together, we outline a number of mitigation strategies that can alleviate some of the most obvious attack strategies. Finally, we look at measures that can be taken in the longer term to safeguard the privacy of the citizens.
Modular Remote Communication Protocol Interpreters
Venue: arXiv cs.SE 2017
Year: 2017

We present "endpoints", a library that provides consistent client implementation, server implementation and documentation from a user-defined communication protocol description. The library provides type safe remote invocations, and is modular and extensible. This paper shows how its usage looks like, highlights the Scala features that its implementation relies on, and compares it to other similar tools.
Learning Less-Overlapping Representations
Venue: arXiv cs.LG 2017
Year: 2017

In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regulariza- tion approach that encourages the supports of weight vectors in RL models to have small overlap, by simultaneously promoting near-orthogonality among vectors and sparsity of each vector. We apply the proposed regularizer to two models: neural networks (NNs) and sparse coding (SC), and develop an efficient ADMM-based algorithm for regu- larized SC. Experiments on various datasets demonstrate that weight vectors learned under our regularizer are more interpretable and have better generalization performance.
Improving Function Coverage with Munch: A Hybrid Fuzzing and Directed Symbolic Execution Approach
Venue: arXiv cs.SE 2017
Year: 2017

Fuzzing and symbolic execution are popular techniques for finding vulnerabilities and generating test-cases for programs. Fuzzing, a blackbox method that mutates seed input values, is generally incapable of generating diverse inputs that exercise all paths in the program. Due to the path-explosion problem and dependence on SMT solvers, symbolic execution may also not achieve high path coverage. A hybrid technique involving fuzzing and symbolic execution may achieve better function coverage than fuzzing or symbolic execution alone. In this paper, we present Munch, an open source framework implementing two hybrid techniques based on fuzzing and symbolic execution. We empirically show using nine large open-source programs that overall, Munch achieves higher (in-depth) function coverage than symbolic execution or fuzzing alone. Using metrics based on total analyses time and number of queries issued to the SMT solver, we also show that Munch is more efficient at achieving better function coverage.
Personalized and Occupational-aware Age Progression by Generative Adversarial Networks
Venue: arXiv cs.CV 2017
Year: 2017

Face age progression, which aims to predict the future looks, is important for various applications and has been received considerable attentions. Existing methods and datasets are limited in exploring the effects of occupations which may influence the personal appearances. In this paper, we firstly introduce an occupational face aging dataset for studying the influences of occupations on the appearances. It includes five occupations, which enables the development of new algorithms for age progression and facilitate future researches. Second, we propose a new occupational-aware adversarial face aging network, which learns human aging process under different occupations. Two factors are taken into consideration in our aging process: personality-preserving and visually plausible texture change for different occupations. We propose personalized network with personalized loss in deep autoencoder network for keeping personalized facial characteristics, and occupational-aware adversarial network with occupational-aware adversarial loss for obtaining more realistic texture changes. Experimental results well demonstrate the advantages of the proposed method by comparing with other state-of-the-arts age progression methods.
Obtaining the coefficients of a Vector Autoregression Model through minimization of parameter criteria
Venue: arXiv cs.OH 2017
Year: 2017

VAR models are a type of multi-equation model that have been widely applied in econometrics. With the arrival of Big Data, huge amounts of data are being collected in numerous fields, making feasible the application of these kind of statistical models. Tools exist to tackle this problem, but the large amount of data, along with the availability of computational techniques and high performance systems, advise an in-depth analysis of the computational aspects of VAR, so large models can be solved efficiently with today's computational systems. This work aims to solve a VAR model by obtaining the coefficients through heuristic and metaheuristic algorithms, minimizing one parameter criterion, and also to compare with those coefficients obtained by OLS. Furthermore, we consider different approaches to reduce the time required to find the model like using matrix decompositions (QR or LQ), exploiting matrix structure, using high performance linear algebra subroutines (BLAS and LAPACK) or parallel metaheuristics.
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Venue: arXiv cs.LG 2017
Year: 2017

Deep neural networks have proven remarkably effective at solving many classification problems, but have been criticized recently for two major weaknesses: the reasons behind their predictions are uninterpretable, and the predictions themselves can often be fooled by small adversarial perturbations. These problems pose major obstacles for the adoption of neural networks in domains that require security or transparency. In this work, we evaluate the effectiveness of defenses that differentiably penalize the degree to which small changes in inputs can alter model predictions. Across multiple attacks, architectures, defenses, and datasets, we find that neural networks trained with this input gradient regularization exhibit robustness to transferred adversarial examples generated to fool all of the other models. We also find that adversarial examples generated to fool gradient-regularized models fool all other models equally well, and actually lead to more "legitimate," interpretable misclassifications as rated by people (which we confirm in a human subject experiment). Finally, we demonstrate that regularizing input gradients makes them more naturally interpretable as rationales for model predictions. We conclude by discussing this relationship between interpretability and robustness in deep neural networks.
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible.
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we introduce a method that significantly reduces the character error rates for OCR text obtained from OCRopus models trained on early printed books. The method uses a combination of cross fold training and confidence based voting. After allocating the available ground truth in different subsets several training processes are performed, each resulting in a specific OCR model. The OCR text generated by these models then gets voted to determine the final output by taking the recognized characters, their alternatives, and the confidence values assigned to each character into consideration. Experiments on seven early printed books show that the proposed method outperforms the standard approach considerably by reducing the amount of errors by up to 50% and more.
Classifier Selection with Permutation Tests
Venue: arXiv cs.IR 2017
Year: 2017

This work presents a content-based recommender system for machine learning classifier algorithms. Given a new data set, a recommendation of what classifier is likely to perform best is made based on classifier performance over similar known data sets. This similarity is measured according to a data set characterization that includes several state-of-the-art metrics taking into account physical structure, statis- tics, and information theory. A novelty with respect to prior work is the use of a robust approach based on permutation tests to directly assess whether a given learning algorithm is able to exploit the attributes in a data set to predict class labels, and compare it to the more commonly used F-score metric for evalu- ating classifier performance. To evaluate our approach, we have conducted an extensive experimentation including 8 of the main machine learning classification methods with varying configurations and 65 bi- nary data sets, leading to over 2331 experiments. Our results show that using the information from the permutation test clearly improves the quality of the recommendations.
Boutiques: a flexible framework for automated application integration in computing platforms
Venue: arXiv cs.SE 2017
Year: 2017

We present Boutiques, a system to automatically publish, integrate and execute applications across computational platforms. Boutiques applications are installed through software containers described in a rich and flexible JSON language. A set of core tools facilitate the construction, validation, import, execution, and publishing of applications. Boutiques is currently supported by several distinct virtual research platforms, and it has been used to describe dozens of applications in the neuroinformatics domain. We expect Boutiques to improve the quality of application integration in computational platforms, to reduce redundancy of effort, to contribute to computational reproducibility, and to foster Open Science.
Language Bootstrapping: Learning Word Meanings From Perception-Action Association
Venue: arXiv cs.RO 2017
Year: 2017

We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment. The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.
Design of an Integrated Analytics Platform for Healthcare Assessment Centered on the Episode of Care
Venue: arXiv cs.CY 2017
Year: 2017

Assessing care quality and performance is essential to improve healthcare processes and population health management. However, due to bad system design and lack of access to required data, this assessment is often delayed or not done at all. The goal of our research is to investigate an advanced analytics platform that enables healthcare quality and performance assessment. We used a user-centered design approach to identify the system requirements and have the concept of episode of care as the building block of information for a key performance indicator analytics system. We implemented architecture and interface prototypes, and performed a usability test with hospital users with managerial roles. The results show that by using user-centered design we created an analytical platform that provides a holistic and integrated view of the clinical, financial and operational aspects of the institution. Our encouraging results warrant further studies to understand other aspects of usability.
Hyper Converged Infrastructures: Beyond virtualization
Venue: arXiv cs.CY 2017
Year: 2017

Hyper Convergence has brought virtualization and IT strategies to a new level. Datacenters are undergoing a deep paradigm shift from a hardware-centric to an application-centric approach which leverages on software defined architectures, while IT is more and more being delivered as services rather than assets or products. Throughout different evolving phases since the initial attempts to convergence, the concept has been refined down to a level where,ultimately, a whole datacenter could be fully managed from a centralized single point, abstracting the whole hardware layer and exposing it to the administrators as a transparent pool of resources. This paper analyzes the evolution of infrastructures and tries to dig into the reality and convenience of Hyper Convergence.
Raspberry Pi and Arduino Uno Working together as a Basic Meteorological Station
Venue: arXiv cs.CY 2017
Year: 2017

The present paper describes a novel Raspberry Pi and Arduino UNO architecture used as a meteorological station. One of the advantages of the proposed architecture is the huge quantity of sensors developed for its usage; practically one can find them for any application, and weather sensing is not an exception. The principle followed is to configure Raspberry as a collector for measures obtained from Arduino, transmitting occurs via USB; meanwhile, Raspberry broadcasts them via a web page. For such activity is possible thanks to Raspbian, a Linux-based operating system. It has a lot of libraries and resources available, among them Apache Web Server, that gives the possibility to host a web-page. On it, the user can observe temperature, humidity, solar radiance, and wind speed and direction. Information on the web-page is refreshed each five minute; however, measurements arrive at Raspberry every ten seconds. This low refreshment rate was determined because weather variables normally do not abruptly change. As an additional feature, system stores all information on the log file, this gives the possibility for future analysis and processing.
Lexical-semantic resources: yet powerful resources for automatic personality classification
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we aim to reveal the impact of lexical-semantic resources, used in particular for word sense disambiguation and sense-level semantic categorization, on automatic personality classification task. While stylistic features (e.g., part-of-speech counts) have been shown their power in this task, the impact of semantics beyond targeted word lists is relatively unexplored. We propose and extract three types of lexical-semantic features, which capture high-level concepts and emotions, overcoming the lexical gap of word n-grams. Our experimental results are comparable to state-of-the-art methods, while no personality-specific resources are required.
Population Based Training of Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Neural networks dominate the modern machine learning landscape, but their training and success still suffer from sensitivity to empirical choices of hyperparameters such as model architecture, loss function, and optimisation algorithm. In this work we present \emph{Population Based Training (PBT)}, a simple asynchronous optimisation algorithm which effectively utilises a fixed computational budget to jointly optimise a population of models and their hyperparameters to maximise performance. Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of trying to find a single fixed set to use for the whole course of training. With just a small modification to a typical distributed hyperparameter training framework, our method allows robust and reliable training of models. We demonstrate the effectiveness of PBT on deep reinforcement learning problems, showing faster wall-clock convergence and higher final performance of agents by optimising over a suite of hyperparameters. In addition, we show the same method can be applied to supervised learning for machine translation, where PBT is used to maximise the BLEU score directly, and also to training of Generative Adversarial Networks to maximise the Inception score of generated images. In all cases PBT results in the automatic discovery of hyperparameter schedules and model selection which results in stable training and better final performance.